The present invention relates generally to wideband MIMO-OFDM (Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) systems, and, more particularly, to a method: interpolation based QR decomposition in MIMO-OFDM systems using D-SMC (Deterministic-Sequential Monte Carlo) demodulator with per chunk ordering.
The following works by others are mentioned in the application and referred to by their associated reference:    [1] P. Agarwal, N. Prasad, X. Wang, and M. Madihian, “An enhanced deterministic sequential monte-carlo method for near optimal MIMO demodulation with QAM constellations,” IEEE Trans. Signal Processing., June 2007.    [2] D. Cescato, M. Borgmann, H. Bolcskei, J. Hansen, and A. Burg, “Interpolation-based QR decomposition in MIMO-OFDM receivers,” in Proc. 6th IEEE Workshop on Signal Processing Advances in Wireless Communications, New York, N.Y., 2005.    [3] D. Wubben and K. D. Kammeyer, “Interpolation-based QR decomposition in MIMO-OFDM receivers,” in Proc. ITG Workshop on Smart Antennas, Reisensburg, Ulm, Germany, March 2006.    [4] P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel,” in Proc. of the ISSSE, Pisa, Italy, September 1998, invited.
The deterministic sequential Monte-Carlo (D-SMC) demodulator is one of the most promising demodulators for multiple antenna systems over narrowband fading channels [1]. The extension of the MIMO D-SMC demodulator from the narrowband case to the wideband system based on OFDM requires the computation of QR decomposition for each of the data-tones. The number of data tones can range from 48 (as in IEEE 802.11a/g standards) to 6817 (as in the DVB-T Standard). Interpolation based QR decomposition algorithms were recently proposed in [2] for MIMO-OFDM systems which employ an identical channel independent order of demodulation for all tones and it was shown that significant complexity reduction over the previous brute-force method could be achieved particularly for large number of tones and small channel orders. [3] modified the interpolation based QR decomposition techniques developed in [2], for a MIMO-OFDM system where each transmitter uses an independent SISO encoder and SIC decoding is employed at the receiver. To improve the performance obtained with the SIC decoder [3] suggested a common ordering where one “common” albeit channel dependent permutation is computed for all data tones prior to interpolation. The common ordering rule suggested in [3] was an extension of the sorted QR rule suggested earlier for the narrow-band MIMO channel. Extensions of the SINR maximizing greedy rule derived originally for the narrowband channel in [4] have also been proposed.
Various prior art techniques for QR decomposition are illustrated. The technique of FIG. 1 determines the QR decompositions (QRDs) of the set of basis tones 10. Using the QRDs of the basis tones, the QRDs of all remaining tones are interpolated and determined 11. This technique offers the lowest complexity for many system configurations, but provides the worst performance due to one fixed (channel-independent) order for all tones 12. In the technique of FIG. 2, the set of a basis tones are used to interpolate and determine the channel responses of all remaining tones 20. The optimal order for each tone is determined 21, and the QRD for each tone corresponding to its optimal order is determined 22. This technique offers optimal performance, but has the highest complexity due to per-tone ordering and QR decomposition 23. The technique of FIG. 3 begins with determining a common order using a set of basis tones 30, determining QRDs of the set of basis tones corresponding to the common order 31. Then, using the QRDs of the basis tones, interpolating and determining QRDs of all remaining tones 32. This technique has complexity and performance that are between the techniques diagramed in FIG. 1 and FIG. 2 33. The technique of FIG. 4 uses a set of basis tones to interpolate and determine the channel responses of all remaining tones 40, and then determines the QRD for each tone 41. This technique offers the same performance as the technique of FIG. 1, and for some system configurations it has the lowest complexity 42.
As noted above, the common ordering rule can result in good performance gains and is the best that can be done with the SIC decoder. The post-decoding feedback stage in the SIC decoder does not allow for per-tone ordering rules. On the other hand the D-SMC demodulator based receiver has no such restriction and in fact benefits more from (finer) per-tone based ordering. However, interpolation based QR decomposition algorithms do not provide any complexity reductions (in-fact can increase the complexity!) when per-tone ordering is employed. Thus there is a tradeoff involved since finer ordering (per-tone as opposed to common) results in better performance but at higher processing complexity (separate QR decomposition for each tone as opposed to interpolation based method).
Accordingly, there is a need for a method which resolves the tradeoff of the above known techniques, using per-chunk ordering and corresponding interpolation based QR decomposition (I-QRD) processes.